{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows how to use the output from VASP DFPT calculation and the phonopy interface to plot the phonon bandstructure.\n",
    "\n",
    "**Requires**: phonopy package (`pip install phonopy`)\n",
    "\n",
    "**Author**: Kiran Mathew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "\n",
    "import pymatgen as pmg\n",
    "from pymatgen.io.vasp.outputs import Vasprun\n",
    "\n",
    "from phonopy import Phonopy\n",
    "from phonopy.structure.atoms import Atoms as PhonopyAtoms\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the primitive cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Si_primitive = PhonopyAtoms(symbols=['Si'] * 2,\n",
    "                            scaled_positions=[(0, 0, 0), (0.75, 0.5, 0.75)] )\n",
    "Si_primitive.set_cell([[3.867422 ,0.000000, 0.000000],\n",
    "                       [1.933711, 3.349287, 0.000000],\n",
    "                       [-0.000000, -2.232856, 3.157737]])\n",
    "\n",
    "# supercell size\n",
    "scell = [[2,0,0],[0,2,0],[0,0,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result from VASP DFPT calculations using the supercell structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vrun = Vasprun(os.path.join(os.path.dirname(pmg.__file__), \"..\", 'test_files', \"vasprun.xml.dfpt.phonon\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize phonopy and set the force constants obtained from VASP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "phonon = Phonopy(Si_primitive, scell)\n",
    "phonon.set_force_constants(vrun.force_constants)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Define the paths for plotting the bandstructure and set them in phonopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bands = []\n",
    "\n",
    "# path 1\n",
    "q_start  = np.array([0.5, 0.5, 0.0])\n",
    "q_end    = np.array([0.0, 0.0, 0.0])\n",
    "band = []\n",
    "for i in range(51):\n",
    "    band.append(q_start + (q_end - q_start) / 50 * i)\n",
    "bands.append(band)\n",
    "\n",
    "# path 2\n",
    "q_start  = np.array([0.0, 0.0, 0.0])\n",
    "q_end    = np.array([0.5, 0.0, 0.0])\n",
    "band = []\n",
    "for i in range(51):\n",
    "    band.append(q_start + (q_end - q_start) / 50 * i)\n",
    "bands.append(band)\n",
    "\n",
    "phonon.set_band_structure(bands)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9xeHllzO3V69bB7RrR7PS5Zfn3fiM3PPuu8DIkQzKKF3a6dEYfhKoKKbqAC5U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\n96tToVuMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ad64950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phonon.plot_band_structure().show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
